Applying Multimodal Large Language Models for Visual Question Answering: Toward Vietnamese Educational Reasoning Systems (International Journal of Innovative Computing, 2026)
Visual Question Answering (VQA) is rapidly advancing due to Multimodal Large Language Models (MLLMs), which demonstrate powerful complex reasoning capabilities. However, this progress is predominantly centered on English and general-domain contexts. Domain-specific fields, such as STEM education, and low-resource languages, such as Vietnamese, remain significantly underserved, lacking both standardized datasets and specialized reasoning models. This research addresses this gap by investigating how MLLMs can be adapted for Vietnamese educational settings. As a foundational step, the ViPPS dataset (Vietnamese Physics Problem Solving) has been constructed and publicly released, the first multimodal dataset for physics problem solving in Vietnamese. Initial experiments on ViPPS show that current MLLMs achieve promising results, but still struggle with domain-specific reasoning and numerical accuracy. Based on these observations, the next stage of this research will focus on expanding the ViPPS dataset in both scale and scope, developing and evaluating advanced text-image reasoning and calculation mechanisms and extending these capabilities to VideoQA and model explainability. This research will contribute critical resources and methods, advancing the field of educational VQA for low-resource languages.
Keywords: Visual Question Answering (VQA), Multimodal Large Language Models (MLLMs), STEM Education, Visual Reasoning, Chain-of-Thought Reasoning